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Adhesive bond strength evaluation in composite materials by laser-generated high amplitude ultrasound

2011· article· en· W2025027270 on OpenAlexaff
Mathieu Perton, Alain Blouin, J.‐P. Monchalin

Bibliographic record

VenueJournal of Physics Conference Series · 2011
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsNational Research Council Canada
FundersCentre National de la Recherche Scientifique
KeywordsMaterials scienceAdhesiveBond strengthComposite materialUltrasonic sensorLaserDelamination (geology)Ultimate tensile strengthComposite numberComposite laminatesStress (linguistics)Ultrasonic testingAmplitudeReflection (computer programming)OpticsAcoustics

Abstract

fetched live from OpenAlex

Adhesive bonding of composites laminates is highly efficient but is not used for joining primary aircraft structures, since there is presently no nondestructive inspection technique to ensure the quality of the bond. We are developing a technique based on the propagation of high amplitude ultrasonic waves to evaluate the adhesive bond strength. Large amplitude compression waves are generated by a short pulse powerful laser under water confinement and are converted after reflection by the assembly back surface into tensile waves. The resulting tensile stresses can cause a delamination inside the laminates or at the bond interfaces. The adhesion strength is evaluated by increasing the laser pulse energy until disbond. A good bond is unaffected by a certain level of stress whereas a weaker one is damaged. The method is shown completely non invasive throughout the whole composite assembly. The sample back surface velocity is measured by an optical interferometer and used to estimate stress history inside the sample. The depth and size of the disbonds are revealed by a post-test inspection by the well established laser-ultrasonic technique. Experimental results show that the proposed method is able to differentiate weak bond from strong bonds and to estimate quantitatively their bond strength.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.737

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.241
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2011
Admission routes1
Has abstractyes

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